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7/27/2019 F311_1CapStudyTemplate
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Study Date
Part Number
Operation
Sequence
Seq. Descr.
Department
Machine
Nominal Spec. Note:Use the examples below to help you decide how to input the nominal and specs.
+ Tol. or USL
- Tol. or LSL
Unitsnm
Typeb Note: Enter "b" or leave blank for bilateral tolerances. High-lite cell to activate pulldown.
Enter "u" for LSL bounded unilateral tolerances. High-lite cell to activate pulldown.
Multiplier1 Note: The multiplier and additive allow you to enter coded data below. Each
Additive0 data point is first multiplied by the multiplier and then the additive is
added to the result. If entering actual values, use the defaults (1 and 0).
Comments:
Force NormalYes Note: Enter "Y" to calculate capability indices based on a normal distribution.
Data Points
Piece # Value Actual Values
1 =
2 =
3 =
4 =
5 =
6 =
7 =
8 =
9 =
10 =11 =
12 =
13 =
14 =
15 =
16 =
17 =
18 =
19 =
20 =
21 =
22 =
23 =
24 =
25 =
26 =
27 =
28 =
29 =
30 =
31 =
32 =
33 =
34 =
35 =
36 =
37 =
38 =
39 =40 =
Nominal Spec. 24.536
+ Tol. or USL 0.05
- Tol. or LSL -0.05
Nominal Spec.
+ Tol. or USL 24.586
- Tol. or LSL 24.486
Nominal Spec.
+ Tol. or USL 586
- Tol. or LSL 486
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JDWW Process Capability Study0
General Statistics: ( 0 data points, 3 pc subgroups ) Date of Study: 00Jan00
Minimum = 0.0000 Mean = 0.0000 Maximum = 0.0000
Lower Process Limit = Targeting = 0.0000 Upper Process Limit =
Lower Spec. Limit = -0.5000 Nominal = 0.0000 Upper Spec. Limit = 0.5000
Std. Dev = 1.000000 Skewness =
6 Sigma = Total Tol. = Kurtosis =
Process Capability Indices (using +/- 3 Sigma for Normal data):
Cp = 1.0000 Cpl = Z(LSL) =
Cr = Cpu = Z(USL) =
PPM (Total) = Cpk = 1.3300 Z(Min) = 0.0000
PPM or () will be under the LSL value of -0.5000
PPM or () will be over the USL value of 0.5000
The amount of variation is acceptable, 1.00 Cp. After adjusting the inherent capability, Cp, to 1.33, the
target error , 0.0000, is acceptable.
0
0.2
0.4
0.6
0.8
1
1.2
1 2
Averages are in control.
Xbar Control Chart
X bar
UCL
X barbar
LCL
SubGrp Size = 3
0
0.2
0.4
0.6
0.8
1
1.2
1 2
Ranges are in control.
Range Control Chart
Range
UCL
Rbar
LCL
0 2 4 6
Capability Plot
Data
Proc
Spec
0
0.2
0.4
0.6
0.8
1
1.2Individuals Plot
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
1.2000
1
The population distribution should be considered NORMAL.
Normal Probability Plot
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
0.
0000
Frequency
Bin
Histogram
F311.1 - Revised, GE, 31Aug05 Printed 12/20/2013 Printed copies are uncontrolled documents and may not be current. Page 2 of
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Data Values Average Upper Spec Lower Spec % Z Ordered Data Statistic
# Data Poin
US
Targ
LS
Total Tolerancmea
media
Standard Deviatio
Upper Proc. Lim
Lower Proc. Lim
6 Sigm
C
C
Cp
C
Cp
Skewnes
Kurtos
Slop
InterceZ(US
Z(LS
% Over US
%Under LS
PPM Over US
PPM Under LS
PPM Tot
Shapiro-Wilks Norm
k
W
A2(Critical W)
Normal
Evaluation
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Johnson Transformation Calculations for Standard CS (raw data):
Cumulative Probabilities Chart Val unbounded bounded lognormal
z 0.524 h
P(-3z) 0.058 g
P(-z) 0.300 l
P(z) 0.700 e
P(3z) 0.942 Data is Normal
Upper Proc. Bnd (Up)
Sample Percentiles Lower Proc. Bnd (Lp)
x(-3z) Median Target Value
x(-z) Cp
x(z) Cr
x(3z) Cpu
Cpl
Johnson Curve Cpk
m Z(USL)
n Z(LSL)
p % Over USL
mn/p2 % Under LSL
type PPM Over USL
PPM Under LSL
PPM Total
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Histogram Data
Raw Data Raw Data
bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7 bin 8 bin 9 bin 10
Data Points 0
Range (R) 0.0000
Classes (K) 10
Class Width 0.0000
Bin NormDist Frequency Z
0.0000
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000 0
0.0000
0
Z Values
Norm Jns Su Jns Sb Jns Sl
Normsdist Values
Norm Jns Su Jns Sb Jns Sl
F311.1 Page 5
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Subgroup Data Range Control Chart
SG 3 Piece 4 Piece 5 Piece SG Range UCL Rbar LCL
# Range X bar Range X bar Range X bar
1
2
3
4
5
6
7
8
9
10
11
12
13 In Control = Yes
R bar X bar bar R bar X bar bar R bar X bar bar
Constants Table Statistics
n D3 D4 A2 d2 n 3
2 0.000 3.267 1.880 1.128 R bar
3 0.000 2.574 1.023 1.693 UCL R
4 0.000 2.282 0.729 2.059 LCL R
5 0.000 2.114 0.577 2.326 X bar bar
UCL X bar
LCL X bar
A2*Rbar
numsubg 0
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Shapiro-Wilk normality test. Calculation of W and comparison to critical value.
W = b / S if n is even, n = 2k; b = an(yn-y1) + an-1(yn-1-y2) + ak+1(yk+1-yk)
if n is odd, n = 2k - 1 ; b = an(yn-y1) + an-1(yn-1-y2) + ak+2(yk+2-yk)
y are the ordered (smallest to largest) data values
a are factors tabled in the SW Tables tab of this workbook
S is the sum of squares of y; S2= (y1-ybar)
2+ (y2-ybar)
2+ +(yn-ybar)
2
The SW test is a lower tail test. Small values indicate non-normality.
b =
S2= 95% Confidence Level
W = W0.95 =
n = 0
k
Data Values
Ordered L
to S
ata
Values
Ordered
S to L yn-i+1-yi
Tabled
Value of
an-i+1
F311.1
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Tables from Shapiro and Wilk, Biometrika , Vol 52, pp 591-611.
i \ n 2 3 4 5 6 7 8 9
1 0.7071 0.7071 0.6872 0.6646 0.6431 0.6233 0.6052 0.5888
2 0.0000 0.1677 0.2413 0.2806 0.3031 0.3164 0.3244
3 0.0000 0.0875 0.1401 0.1743 0.1976
4 0.0000 0.0561 0.0947
5 0.0000
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Percentage points of the W test* for n = 3(1)50
\ p 0.99 0.98 0.95 0.90 0.50 0.10 0.05 0.02
n \ (1-p) 0.01 0.02 0.05 0.10 0.50 0.90 0.95 0.98
3 0.753 0.756 0.767 0.789 0.959 0.998 0.999 1.000
4 0.687 0.791 0.748 0.792 0.935 0.987 0.992 0.996
5 0.686 0.715 0.762 0.806 0.927 0.979 0.986 0.991
6 0.713 0.743 0.788 0.826 0.927 0.974 0.981 0.986
7 0.730 0.760 0.803 0.838 0.928 0.972 0.979 0.985
8 0.749 0.778 0.818 0.851 0.932 0.972 0.978 0.984
9 0.764 0.791 0.829 0.859 0.935 0.972 0.978 0.984
10 0.781 0.806 0.842 0.869 0.938 0.972 0.978 0.983
11 0.792 0.817 0.850 0.876 0.940 0.973 0.979 0.984
12 0.805 0.828 0.859 0.883 0.943 0.973 0.979 0.984
13 0.814 0.837 0.866 0.889 0.945 0.974 0.979 0.984
14 0.825 0.846 0.874 0.895 0.947 0.975 0.980 0.984
15 0.835 0.855 0.881 0.901 0.950 0.975 0.980 0.984
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16 0.844 0.863 0.887 0.906 0.952 0.976 0.981 0.985
17 0.851 0.869 0.892 0.910 0.954 0.977 0.981 0.985
18 0.858 0.874 0.897 0.914 0.956 0.978 0.982 0.986
19 0.863 0.879 0.901 0.917 0.957 0.978 0.982 0.986
20 0.868 0.884 0.905 0.920 0.959 0.979 0.983 0.986
21 0.873 0.888 0.908 0.923 0.960 0.980 0.983 0.987
22 0.878 0.892 0.911 0.926 0.961 0.980 0.984 0.987
23 0.881 0.895 0.914 0.928 0.962 0.981 0.984 0.987
24 0.884 0.898 0.916 0.930 0.963 0.981 0.984 0.987
25 0.888 0.901 0.918 0.931 0.964 0.981 0.985 0.988
26 0.891 0.904 0.920 0.933 0.965 0.982 0.985 0.988
27 0.894 0.906 0.923 0.935 0.965 0.982 0.985 0.988
28 0.896 0.908 0.924 0.936 0.966 0.982 0.985 0.988
29 0.898 0.910 0.926 0.937 0.966 0.982 0.985 0.988
30 0.900 0.912 0.927 0.939 0.967 0.983 0.985 0.988
31 0.902 0.914 0.929 0.940 0.967 0.983 0.986 0.988
32 0.904 0.915 0.930 0.941 0.968 0.983 0.986 0.988
33 0.906 0.917 0.931 0.942 0.968 0.983 0.986 0.989
34 0.908 0.919 0.933 0.943 0.969 0.983 0.986 0.989
35 0.910 0.920 0.934 0.944 0.969 0.984 0.986 0.989
36 0.912 0.922 0.935 0.945 0.970 0.984 0.986 0.989
37 0.914 0.924 0.936 0.946 0.970 0.984 0.987 0.989
38 0.916 0.925 0.938 0.947 0.971 0.984 0.987 0.989
39 0.917 0.927 0.939 0.948 0.971 0.984 0.987 0.989
40 0.919 0.928 0.940 0.949 0.972 0.985 0.987 0.989
41 0.920 0.929 0.941 0.950 0.972 0.985 0.987 0.989
42 0.923 0.930 0.942 0.951 0.972 0.985 0.987 0.989
43 0.924 0.932 0.943 0.951 0.973 0.985 0.987 0.990
44 0.925 0.933 0.944 0.952 0.973 0.985 0.987 0.990
45 0.926 0.934 0.945 0.953 0.973 0.985 0.988 0.990
46 0.927 0.935 0.945 0.953 0.974 0.985 0.988 0.990
47 0.928 0.936 0.946 0.954 0.974 0.985 0.988 0.990
48 0.929 0.937 0.947 0.954 0.974 0.985 0.988 0.990
49 0.929 0.937 0.947 0.955 0.974 0.985 0.988 0.99050 0.930 0.938 0.947 0.955 0.974 0.985 0.988 0.990
* Based on fitted Johnson (1949) SBapproximation, see Shapiro and Wilk (Biometrika 1965) for details
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10 11 12 13 14 15 16 17 18
0.5739 0.5601 0.5475 0.5359 0.5251 0.5150 0.5056 0.4968 0.4486
0.3291 0.3315 0.3325 0.3325 0.3318 0.3306 0.3290 0.3273 0.3253
0.2141 0.2260 0.2347 0.2412 0.2460 0.2495 0.2521 0.2540 0.2553
0.1224 0.1429 0.1586 0.1707 0.1802 0.1878 0.1939 0.1988 0.2027
0.0399 0.0695 0.0922 0.1099 0.1240 0.1353 0.1477 0.1524 0.1587
0.0000 0.0303 0.0539 0.0727 0.0880 0.1005 0.1109 0.1197
0.0000 0.0240 0.0433 0.0593 0.0725 0.0837
0.0000 0.0196 0.0359 0.0496
0.0000 0.0163
0.01
0.99
1.000
0.997
0.993
0.989
0.988
0.987
0.986
0.986
0.986
0.986
0.986
0.986
0.987
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Coefficients {an-i+1} for the W test for normality for n = 2(1)50
19 20 21 22 23 24 25 26 27
0.4808 0.4734 0.4643 0.4590 0.4542 0.4493 0.4450 0.4407 0.4366
0.3232 0.3211 0.3185 0.3156 0.3126 0.3098 0.3069 0.3403 0.3018
0.2561 0.2565 0.2578 0.2571 0.2563 0.2554 0.2543 0.2533 0.2522
0.2059 0.2085 0.2119 0.2131 0.2139 0.2145 0.2148 0.2151 0.2152
0.1641 0.1686 0.1736 0.1764 0.1787 0.1807 0.1822 0.1836 0.1848
0.1271 0.1334 0.1399 0.1443 0.1480 0.1512 0.1539 0.1563 0.1584
0.0932 0.1013 0.1092 0.1150 0.1201 0.1245 0.1283 0.1316 0.1346
0.0612 0.0711 0.0804 0.0878 0.0941 0.0997 0.1046 0.1089 0.1128
0.0303 0.0422 0.0530 0.0618 0.0696 0.0764 0.0823 0.0876 0.0923
0.0000 0.0140 0.0263 0.0368 0.0459 0.0539 0.0601 0.0672 0.0728
0.0000 0.0122 0.0228 0.0321 0.0403 0.0476 0.0540
0.0000 0.0107 0.0200 0.0284 0.0358
0.0000 0.0094 0.0178
0.0000
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46 47 48 49 50
0.3830 0.3808 0.3789 0.3770 0.3751
0.2635 0.2620 0.2604 0.2589 0.2574
0.2302 0.2291 0.2281 0.2271 0.2260
0.2058 0.2052 0.2045 0.2038 0.2032
0.1862 0.1859 0.1855 0.1851 0.1847
0.1695 0.1695 0.1693 0.1692 0.1619
0.1548 0.1550 0.1551 0.1553 0.1554
0.1415 0.1420 0.1423 0.1427 0.1430
0.1293 0.1300 0.1306 0.1312 0.1317
0.1180 0.1189 0.1197 0.1205 0.1212
0.1073 0.1085 0.1095 0.1105 0.1113
0.0972 0.0986 0.0998 0.1010 0.1020
0.0876 0.0892 0.0906 0.0919 0.0932
0.0783 0.0801 0.0817 0.0832 0.0846
0.0694 0.0713 0.0731 0.0748 0.0764
0.0607 0.0628 0.0648 0.0667 0.0685
0.0522 0.0546 0.0568 0.0588 0.0608
0.0439 0.0465 0.0489 0.0511 0.0532
0.0357 0.0385 0.0411 0.0436 0.0459
0.0277 0.0307 0.0335 0.0361 0.0386
0.0197 0.0229 0.0259 0.0288 0.0314
0.0118 0.0153 0.0185 0.0215 0.0244
0.0039 0.0076 0.0111 0.0143 0.0174
0.0000 0.0037 0.0071 0.0104
0.0000 0.0035
F311 1